Fullstack Learning Hydra

On apps like tinder, we are parameterizing our relationships. We can swipe left or right based on any number of factors and the app will give us ever-appealing options. A mutual match, when formed, has the chance to become something great. Enabling this is an open access policy to the unit of value in that network – the connection between mutually interested parties.

There is a weird and wonderful corner of twitter that acts like a “Tinder for Brains”, where learners are matched to a subject matter expert and information is allowed to flow freely. It is democratizing access to experts in a way not known before.

A minimal barrier of entry to the ‘network of experts’ is following them, which gives you direct access to their thoughts; their public comments give insight into their expertise. It also gives you the ability to decide after some research whether to take the next step in your learning journey.

This access to people’s expertise comes with consequences. By broadening the definition and allowing for indirect indicators of expertise, people are inheriting not just expertise but culture. This exacerbates our biases and creates distractions.

Here is a relationship hydra with each vertex showing a different goal. The number of people corresponds directly to the width of the triangle.

A tinder match, like a connection on tinder for brains, may lead to different outcomes. The difference is that there is a support structure of entities (contractual, legal, utilitarian, platonic, redress, social, religious, and political) for taking advantage of each of those outcomes.

While full-stack ‘tinder to long term romantic relationship’ app would need a culture change to be successful, the ‘tinder for brains” to long term intellectual relationship app does not, and is in fact lauded.

This learning Hydra, similar to the relationship hydra, has vertices for each long term learning goal. The width of each head represents the number of people wanting to reach that goal.

Our current frontiers of learning at scale i.e. MOOC’s( Massively Open Online Course) are at best a half solution. MOOC’s serve self-selection of topics and are self-paced but prescriptive. This creates a unique set of issues like class completion rate, Accreditation for the MOOC, Jobs for people going through the MOOC. MOOC’s have an online community aspect that leaves much to be desired. The deliverables in most MOOCs are unsophisticated and do not provide much incentive for full-stack learning.

A big cause for these problems is the impersonal, non-goal-oriented, improperly incentivized user acquisition funnel. It is wide and not optimized for self-guided learning. our “tinder for brains” hydra solves this by allowing people to choose their level of interaction, the path to success, and ultimate goals.

One question that arises is what incentive exists at the top of this pyramid, head of the hydra, to pull people up? Let’s explore those incentives from two of the multiple heads Private sector Jobs and Academia.

Private Jobs :

The recruitment process, whether through HR firms, on-demand startups, or white glove executive recruitment firms, is uncertain, personal network-oriented and slow.

Our Learning hydra has at each of its stages

  1. a series of verifiable deliverables,

  2. with credit going to the right people, like a commit record on Github.

Our learning hydra with its proof of work and contribution will help in the move towards a more open, structured and audit-able recruiting process. This, in turn, will improve diversity in the workplace and bring a meritocratic process.

Academia :

The Ivory tower of academia has hiring practices that are not oriented towards openness. The academic head of our learning hydra has underneath is a list of work products by the people who want to enter this field. This acts as a funnel for people with open access prints, verified work, qualifying them for research or teaching.

A record of work products and contributions like github-commit is a powerful thing. It has the ability to bolster the credentials of people and allows them to make decisions based on this record. It forces gate-keepers to face their biases and acts as a proof point for third party arbitration in cases where discrimination is evident.

Like any system, this can be misused if proper access control mechanisms are not in place. That same record of deliverables can be used against someone akin to employers looking at Facebook pictures of a drunken party, or comments made by politicians and celebrities becoming tabloid fodder.

For this system to succeed there need to be immutable records systems and an open access policy at each level. Such a system is resilient to political changes and promotes a new type of “Full Stack freelancer” who is adept at bouncing from one head of this hydra to another. This creates new challenges in Intellectual property protection, compensation, and tax policy.

The future of learning is moving towards this hydra that, given a bit more discipline and education, we just might tame.